Advertisement

Wireless Personal Communications

, Volume 101, Issue 1, pp 119–141 | Cite as

A Novel Game Theoretic Method for Efficient Downlink Resource Allocation in Dual Band 5G Heterogeneous Network

  • Ramoni O. Adeogun
Article
  • 78 Downloads

Abstract

Hybrid heterogeneous wireless networks utilizing both traditional microwave frequency band and millimetre wave band are currently been investigated as a potential approach to meet the increasing demand for ultra-high rate transmission with the severe microwave spectrum scarcity and requirement for low power network devices. In this paper, we investigate downlink resource allocation in two-tier heterogeneous networks comprising of a macrocell transmitting at a microwave frequency and dual-band small cells utilizing both microwave and millimetre wave frequencies. We present a novel architecture with dual band small cell base stations. The small cell coverage area is divided into two regions where the users in the inner and outer regions are served by the associated small cells on millimetre wave and microwave frequencies, respectively. We formulate a two layer game theory based approach for maximizing energy efficiency and spectral efficiency of the system with optimal usage of available radio resources. The proposed game theoretic approach comprises of a non-cooperative frequency assignment game as its first layer and a multi-objective optimization based game as the second layer. In the frequency assignment game, each small cell base station selects a frequency band from either the microwave band or millimetre wave band for each of its associated users by maximizing the data rate of its users. The solution to the frequency assignment game is obtained via Pure Strategy Nash Equilibrium. The utility function of the game in the second layer involves power and sub-carrier allocation via the joint maximization of both energy efficiency and spectral efficiency of the network. The utility function is formulated as a multi-objective optimization problem which is converted into a single objective problem and solved using Lagrangian dual relaxation. Simulations results show that the proposed dual band heterogeneous network with game theoretic resource allocation offers improved sum rate, energy efficiency and spectral efficiency compared to classical shared spectrum heterogeneous network utilizing only microwave frequency band.

Keywords

Heterogeneous network Millimetre wave 5G Interference coordination Optimization Non-cooperative game Resource allocation OFDMA 

Notes

Acknowledgements

The authors would like to thank the Department of Electrical Engineering, University of Cape Town for providing funding for this research through the departmental Postdoctoral Fellowship. Many thanks to Associate Prof. Mqhele Dlodlo for providing feedback on the manuscript.

References

  1. 1.
    Rappaport, T., Murdock, J., & Gutierrez, F. (2011). State of the art in 60-GHz integrated circuits and systems for wireless communications. Proceedings of the IEEE, 99(8), 1390–1436.CrossRefGoogle Scholar
  2. 2.
    Rappaport, T., Gutierrez, F., Ben-Dor, E., Murdock, J., Qiao, Y., & Tamir, J. (2013). Broadband millimeter-wave propagation measurements and models using adaptive-beam antennas for outdoor urban cellular communications. IEEE Transactions on Antennas and Propagation, 61(4), 1850–1859.CrossRefGoogle Scholar
  3. 3.
    Ericsson. (2011). Traffic and market data report. https://www.ericsson.com/assets/local/news/2012/2/tmd_report_feb_web.pdf.
  4. 4.
    Pi, Z., & Khan, F. (2011). An introduction to millimeter-wave mobile broadband systems. IEEE Communications Magazine, 49(6), 101–107.CrossRefGoogle Scholar
  5. 5.
    Rappaport, T., Sun, S., Mayzus, R., Zhao, H., Azar, Y., Wang, K., et al. (2013). Millimeter wave mobile communications for 5G cellular: It will work!. IEEE Access, 1, 335–349.CrossRefGoogle Scholar
  6. 6.
    Hasan, Monowar, & Hossain, Ekram. (2016). Distributed resource allocation in 5G cellular networks (pp. 129–161). Hoboken: Wiley.  https://doi.org/10.1002/9781118979846.ch8.Google Scholar
  7. 7.
    Kuang, Q., Utschick, W., & Dotzler, A. (2016). Optimal joint user association and multi-pattern resource allocation in heterogeneous networks. IEEE Transactions on Signal Processing, 64(13), 3388–3401.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Mohjazi, L. S., Al-Qutayri, M. A., Barada, H. R., Poon, K. F., & Shubair, R. M. (2012). Self-optimization of pilot power in enterprise femtocells using multi objective heuristic. Journal of Computer Networks and Communications.  https://doi.org/10.1155/2012/303465.
  9. 9.
    Yang, K., Martin, S., Quadri, D., Wu, J., & Feng, G. (2016). Energy-efficient downlink resource allocation in heterogeneous OFDMA networks. IEEE Transactions on Vehicular Technology, PP(99), 1.CrossRefGoogle Scholar
  10. 10.
    Singh, V., Lentz, M., Bhattacharjee, B., La, R. J., & Shayman, M. A. (2016). Dynamic frequency resource allocation in heterogeneous cellular networks. IEEE Transactions on Mobile Computing, 15(11), 2735–2748.CrossRefGoogle Scholar
  11. 11.
    Prez-Romero, J., Snchez-Gonzlez, J., Agust, R., Lorenzo, B., & Glisic, S. (2016). Power-efficient resource allocation in a heterogeneous network with cellular and D2D capabilities. IEEE Transactions on Vehicular Technology, 65(11), 9272–9286.CrossRefGoogle Scholar
  12. 12.
    Maksymyuk, T., Brych, M., & Masyuk, A. (2015). Fractal geometry based resource allocation for 5G heterogeneous networks. In Problems of infocommunications science and technology (PICST), 2015 second international scientific-practical conference (pp. 69–72).Google Scholar
  13. 13.
    Bikov, E., & Botvich, D. (2015). Multi-agent learning for resource allocation dense heterogeneous 5G network. In 2015 International conference on engineering and telecommunication (EnT) (pp. 1–6).Google Scholar
  14. 14.
    Hossain, E., Rasti, M., Tabassum, H., & Abdelnasser, A. (2014). Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective. IEEE Wireless Communications, 21(3), 118–127.CrossRefGoogle Scholar
  15. 15.
    Tefft, J. R., & Kirsch, N. J. (2013). Accelerated learning in machine learning-based resource allocation methods for heterogenous networks. In 2013 IEEE 7th international conference on intelligent data acquisition and advanced computing systems (IDAACS) (Vol. 01, pp. 468–473).Google Scholar
  16. 16.
    Hao, P., Yan, X., Li, J., Li, Y. N. R., & Wu, H. (2015). Flexible resource allocation in 5G ultra dense network with self-backhaul. In 2015 IEEE Globecom Workshops (GC Wkshps) (pp. 1–6).Google Scholar
  17. 17.
    Saeed, A., Katranaras, E., Zoha, A., Imran, A., Imran, M. A., & Dianati, M. (2015). Energy efficient resource allocation for 5G heterogeneous networks. In 2015 IEEE 20th international workshop on computer aided modelling and design of communication links and networks (CAMAD), Guildford, 2015, pp. 119–123.  https://doi.org/10.1109/CAMAD.2015.7390492.
  18. 18.
    Munir, H., Hassan, S. A., Pervaiz, H., & Ni, Q. (2016). A game theoretical network-assisted user-centric design for resource allocation in 5G heterogeneous networks. In 2016 IEEE 83rd vehicular technology conference (VTC Spring) (pp. 1–5).Google Scholar
  19. 19.
    Akdeniz, M. R., Liu, Y., Sun, S., Rangan, S., Rappaport, T. S., & Erkip, E. (2013). Millimeter wave channel modeling and cellular capacity evaluation. CoRR (Vol. 2013) (online). arXiv.org/abs/1312.4921
  20. 20.
    Doan, C., Emami, S., Sobel, D., Niknejad, A., & Brodersen, R. (2004). Design considerations for 60 GHz cmos radios. IEEE Communications Magazine, 42(12), 132–140.CrossRefGoogle Scholar
  21. 21.
    Doan, C., Emami, S., Niknejad, A., & Brodersen, R. (2005). Millimeter-wave cmos design. IEEE Journal of Solid-State Circuits, 40(1), 144–155.CrossRefGoogle Scholar
  22. 22.
    Rangan, S., Rappaport, T., & Erkip, E. (2014). Millimeter-wave cellular wireless networks: Potentials and challenges. Proceedings of the IEEE, 102(3), 366–385.CrossRefGoogle Scholar
  23. 23.
    Bogale, T. E., & Le, L. B. (2016). Massive mimo and mmwave for 5G wireless hetnet: Potential benefits and challenges. IEEE Vehicular Technology Magazine, 11(1), 64–75.CrossRefGoogle Scholar
  24. 24.
    Niknam, S., Nasir, A. A., Mehrpouyan, H., & Natarajan, B. (2016). A multiband OFDMA heterogeneous network for millimeter wave 5G wireless applications. IEEE Access, 4, 5640–5648.CrossRefGoogle Scholar
  25. 25.
    Wang, Y., Wang, X., & Wang, L. (2014). Low-complexity stackelberg game approach for energy-efficient resource allocation in heterogeneous networks. IEEE Communications Letters, 18(11), 2011–2014.CrossRefGoogle Scholar
  26. 26.
    Tang, J., So, D. K. C., Alsusa, E., Hamdi, K. A., & Shojaeifard, A. (2015). Resource allocation for energy efficiency optimization in heterogeneous networks. IEEE Journal on Selected Areas in Communications, 33(10), 2104–2117.CrossRefGoogle Scholar
  27. 27.
    Carvalho, G. H. S., Woungang, I., Anpalagan, A., & Hossain, E. (2016). Qos-aware energy-efficient joint radio resource management in multi-rat heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(8), 6343–6365.CrossRefGoogle Scholar
  28. 28.
    Alsharif, M. H., Nordin, R., & Ismail, M. (2013). Survey of green radio communications networks: Techniques and recent advances. Journal of Computer Networks and Communications.  https://doi.org/10.1155/2013/453893.Google Scholar
  29. 29.
    Yasar, L. H., Mischa, D., Amr, M., Mokhtar, G. M., & Fabiana, C. C. (2016). Towards energy-aware 5G heterogeneous networks (pp. 31–44). Berlin: Springer.  https://doi.org/10.1007/978-3-319-27568-02.Google Scholar
  30. 30.
    Chen, R., Liu, L., Sayana, K., & Li, H. (2013). Energy-efficient wireless communications with future networks and diverse devices. Journal of Computer Networks and Communications.  https://doi.org/10.1155/2013/897029.Google Scholar
  31. 31.
    Pervaiz, H., Musavian, L., Ni, Q., & Ding, Z. (2015). Energy and spectrum efficient transmission techniques under QoS constraints toward green heterogeneous networks. IEEE Access, 3, 1655–1671.CrossRefGoogle Scholar
  32. 32.
    Adeogun, R. O. (2018). Joint resource allocation for dual-band heterogeneous wireless network. In IEEE wireless communications and networking conference (WCNC) Google Scholar
  33. 33.
    Akdeniz, M., Liu, Y., Samimi, M., Sun, S., Rangan, S., Rappaport, T., et al. (2014). Millimeter wave channel modeling and cellular capacity evaluation. IEEE Journal on Selected Areas in Communications, 32(6), 1164–1179.CrossRefGoogle Scholar
  34. 34.
    Fudenberg, D., & Tirole, J. (1993). Game theory. Cambridge: MIT Press.zbMATHGoogle Scholar
  35. 35.
    Cui, S., Goldsmith, A. J., & Bahai, A. (2005). Energy-constrained modulation optimization. IEEE Transactions on Wireless Communications, 4(5), 2349–2360.CrossRefGoogle Scholar
  36. 36.
    Amin, O., Bedeer, E., Ahmed, M. H., & Dobre, O. A. (2016). Energy efficiency-spectral efficiency tradeoff: A multiobjective optimization approach. IEEE Transactions on Vehicular Technology, 65(4), 1975–1981.CrossRefGoogle Scholar
  37. 37.
    Dinkelbach, W. (1967). On nonlinear fractional programming. Management Science, 13(7), 492–498.MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Schaible, S. (1976). Fractional programming. II, on dinkelbach’s algorithm. Management Science, 22(8), 868–873.MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Khoshkholgh, M. G., Yamchi, N. M., Navaie, K., Yanikomeroglu, H., Leung, V. C. M., & Shin, K. G. (2015). Radio resource allocation for OFDM-based dynamic spectrum sharing: Duality gap and time averaging. IEEE Journal on Selected Areas in Communications, 33(5), 848–864.CrossRefGoogle Scholar
  40. 40.
    Stefanov, S. M. (2014). On the application of iterative methods of nondifferentiable optimization to some problems of approximation theory. Mathematical Problems in Engineering.  https://doi.org/10.1155/2014/165701.MathSciNetGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Wireless Communication and Networks (WCN) Section, Department of Electronics SystemsAalborg UniversityAalborgDenmark
  2. 2.Department of Electrical EngineeringUniversity of Cape TownCape TownSouth Africa

Personalised recommendations